Clustering (kmeans)¶
Several clustering methods are available in the EnMAP-Box. You can find them in the Processing Toolbox under Fit … algorithm first and then apply it to an image with Predict (unsupervised) classification layer.
. The usual way to apply these methods is to use aThis recipe demonstrates the basic workflow of applying clusterers using K-Means clustering (Fit KMeans) and the test dataset.
See also
You can find all the available clustering algorithms here.
Open the test dataset
In the processing toolbox go to
- Specify
enmap_berlin.bsq
under Raster - Under Output Clusterer specify an output file path and click Run
- Specify
Now open
- Select
enmap_berlin.bsq
as input Raster - Under Clusterer click … and select the output
.pkl
file from the Fit KMeans algorithm - Specify an output filepath for the transformed raster under Clustering and click Run
- Select
Tip
8 clusters is the default of the kmeans algorithm here, if you want to change the number of clusters, run the
Fit Kmeans algorithm with a fewer number, by altering the KMeans()
function in the Code window to KMeans(n_clusters=4)
.
This will reduce the amount of clusters to 4.